Wildlife Loss Since 1970 Is Uneven — and Extreme in Freshwater Systems

A Makeover Monday visualization redesigning the Living Planet Index chart from ZSL & WWF. The makeover reframes the data as percentage lost, ranks all eight categories by severity, and reveals freshwater ecosystems as the primary driver of the most extreme regional declines.

Original

The original visualization comes from Living Planet Index

Original visualization

Makeover

Figure 1: A combined dashboard showing global declines in wildlife populations from the Living Planet Index (1970–2020). A horizontal bar chart ranks eight categories by the percentage of population lost, from Latin America & the Caribbean (95% lost) to Europe & Central Asia (35% lost), with a dashed line marking the 73% global average. Below, line charts show the global trend declining from 100% to 27%, with ecosystem breakdowns revealing freshwater species (15% remaining) declined far more steeply than terrestrial (31%) or marine (44%) systems.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse, ggtext, showtext, scales, glue,
  janitor, lubridate, patchwork
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 12,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

lpi_raw <- readxl::read_excel(
  here::here("data/MakeoverMonday/2026/LPI 2024.xlsx")) |>
  clean_names()
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(lpi_raw)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |-  prepare data ----
lpi_data <- lpi_raw |>
  mutate(
    category = str_trim(category),
    lpi_pct = lpi_final * 100,
    ci_low = ci_low * 100,
    ci_high = ci_high * 100,
    category_label = case_when(
      category == "Europe and Central Asia" ~ "Europe & Central Asia",
      category == "Latin Amercia & Caribbean" ~ "Latin America & Caribbean",
      TRUE ~ category
    )
  )

endpoints <- lpi_data |>
  filter(year == 2020) |>
  mutate(
    pct_remaining = round(lpi_pct),
    pct_lost = round(100 - lpi_pct)
  )

global_end <- endpoints |> filter(category == "Global")
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    danger        = "#C0392B",
    primary       = "#2C3E50",
    freshwater    = "#2BA8A0",
    terrestrial   = "#8E6F47",
    marine        = "#2471A3",
    neutral_dark  = "#5D6D7E",
    neutral_mid   = "#AEB6BF",
    highlight     = "#E67E22"
  )
)

### |-  Main titles ----
title_text <- NULL

subtitle_text <- NULL

caption_text <- create_mm_caption(
  mm_year = 2026, mm_week = 07,
  source_text = glue(
    "Bars show % lost; lines show % remaining.<br>",
    "Living Planet Index tracks population trends, not species counts.<br>",
    "ZSL & WWF Living Planet Index 2024 | ",
    "34,836 populations across 5,495 vertebrate species"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.3), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.8), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    # axis.line.x = element_line(color = "#252525", linewidth = .1),
    # axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- PANEL 1 — DAMAGE REPORT (% LOST) ----

bars <- endpoints |>
  filter(category != "Global") |>
  mutate(
    category_label = fct_reorder(category_label, pct_lost),
    bar_color = case_when(
      pct_lost >= 80 ~ colors$palette$danger,
      pct_lost >= 60 ~ colors$palette$highlight,
      TRUE ~ colors$palette$neutral_dark
    )
  )

p1 <- ggplot(bars, aes(pct_lost, category_label)) +
  # Geoms
  geom_col(aes(fill = bar_color), width = 0.6) +
  geom_text(
    aes(label = paste0(pct_lost, "% lost")),
    hjust = -0.08,
    size = 3.5,
    fontface = "bold",
    family = fonts$text,
    color = colors$palette$primary
  ) +
  geom_vline(
    xintercept = global_end$pct_lost,
    linetype = "dashed",
    linewidth = 0.4,
    color = colors$palette$primary
  ) +
  annotate(
    "text",
    x = global_end$pct_lost - 1,
    y = 0.6,
    label = glue("Global avg: {global_end$pct_lost}% lost"),
    hjust = 1,
    size = 2.8,
    family = fonts$text,
    color = colors$palette$primary
  ) +
  # Scales
  scale_fill_identity() +
  scale_x_continuous(
    limits = c(0, 110),
    breaks = seq(0, 100, 25),
    labels = ~ paste0(.x, "%")
  ) +
  # Labs
  labs(
    title = "Wildlife Loss Since 1970 Is Uneven — and Extreme in Freshwater Systems",
    subtitle = glue(
      "% of monitored vertebrate populations lost by 2020 | ",
      "Living Planet Index (1970 = 100 baseline)"
    ),
    x = "% of 1970 population lost",
    y = NULL
  )

### |- PANEL 2 — ECOSYSTEM TRENDS (% REMAINING) ----
make_panel <- function(cat, col, bold = FALSE) {
  d <- lpi_data |> filter(category == cat)
  ep <- endpoints |> filter(category == cat)

  ggplot(d, aes(year)) +
    # Geoms
    geom_ribbon(aes(ymin = ci_low, ymax = ci_high),
      fill = col, alpha = 0.12
    ) +
    geom_hline(
      yintercept = 100, linetype = "dotted",
      color = colors$palette$neutral_dark, linewidth = 0.2
    ) +
    geom_line(aes(y = lpi_pct), color = col, linewidth = 1) +
    geom_point(
      data = d |> filter(year == 2020),
      aes(y = lpi_pct),
      color = col,
      size = 2
    ) +
    annotate(
      "text",
      x = 2022,
      y = ep$lpi_pct,
      label = paste0(ep$pct_remaining, "%"),
      hjust = 0,
      size = 3.2,
      fontface = "bold",
      family = fonts$text,
      color = col
    ) +
    # Scales
    scale_y_continuous(
      limits = c(0, 115),
      breaks = c(0, 50, 100),
      labels = ~ paste0(.x, "%")
    ) +
    scale_x_continuous(
      breaks = c(1970, 1995, 2020),
      expand = expansion(mult = c(0.02, 0.15))
    ) +
    # Labs
    labs(
      title = cat,
      x = NULL,
      y = NULL
    ) +
    # Theme
    theme(
      plot.title = element_text(
        face = ifelse(bold, "bold", "plain"),
        family = fonts$title
      ),
      panel.grid.major.y = element_line(color = "gray92", linewidth = 0.3),
      panel.grid.major.x = element_blank(),
      panel.grid.minor = element_blank(),
      axis.ticks = element_blank()
    )
}

p2_global <- make_panel("Global", colors$palette$primary)
p2_terr <- make_panel("Terrestrial", colors$palette$terrestrial)
p2_fresh <- make_panel("Freshwater", colors$palette$freshwater, bold = TRUE)
p2_marine <- make_panel("Marine", colors$palette$marine)

p2 <- (p2_global / (p2_terr | p2_fresh | p2_marine)) +
  plot_annotation(
    subtitle = "Lines show % of populations remaining since 1970",
    theme = theme(
      plot.subtitle = element_text(
        family = fonts$text,
        size = 10,
        color = colors$palette$neutral_dark,
        margin = margin(b = 10)
      ),
      panel.grid.major.y = element_line(color = "gray92", linewidth = 0.3),
      panel.grid.major.x = element_blank(),
      panel.grid.minor = element_blank(),
      axis.ticks = element_blank()
    )
  )

### |- COMBINED PLOTS ----
combined_plots <- (p1 / p2) +
  plot_layout(heights = c(1.2, 1.3)) +
  plot_annotation(
    caption = caption_text,
    theme = theme(
      plot.title = element_markdown(
        size = rel(1.4),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.15,
        margin = margin(t = 0, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.8),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.88),
        lineheight = 1.5,
        margin = margin(t = 5, b = 25)
      ),
      plot.caption = element_markdown(
        size = rel(0.5),
        family = fonts$subtitle,
        color = colors$caption,
        hjust = 0,
        lineheight = 1.4,
        margin = margin(t = 20, b = 5)
      )
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 12
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      patchwork_1.3.2 janitor_2.2.1   glue_1.8.0     
 [5] scales_1.4.0    showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0  
[13] dplyr_1.2.0     purrr_1.2.1     readr_2.1.6     tidyr_1.3.2    
[17] tibble_3.3.1    ggplot2_4.0.2   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.47          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.7  vctrs_0.7.1        tools_4.4.0        generics_0.1.3    
 [9] curl_5.2.1         gifski_1.32.0-2    pkgconfig_2.0.3    ggplotify_0.1.2   
[13] RColorBrewer_1.1-3 S7_0.2.1           readxl_1.4.5       lifecycle_1.0.5   
[17] compiler_4.4.0     farver_2.1.2       textshaping_0.3.7  codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        pillar_1.11.1     
[25] camcorder_0.1.0    magick_2.9.0       commonmark_1.9.1   tidyselect_1.2.1  
[29] digest_0.6.37      stringi_1.8.3      rsvg_2.6.0         rprojroot_2.1.1   
[33] fastmap_1.2.0      grid_4.4.0         cli_3.6.5          magrittr_2.0.4    
[37] withr_3.0.1        timechange_0.4.0   rmarkdown_2.28     cellranger_1.1.0  
[41] hms_1.1.4          evaluate_1.0.0     knitr_1.48         markdown_1.12     
[45] gridGraphics_0.5-1 rlang_1.1.7        gridtext_0.1.5     Rcpp_1.0.13       
[49] xml2_1.5.2         svglite_2.2.2      rstudioapi_0.18.0  jsonlite_2.0.0    
[53] R6_2.5.1           fs_1.6.4           systemfonts_1.3.1 

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_07.qmd.

For the full repository, click here.

10. References

Expand for References

Primary Data (Makeover Monday):

1. Makeover Monday 2026 Week 7: Living Planet Index

2. Original Article: Latest Results - Living Planet Index

   • Source: Zoological Society of London (ZSL) & World Wildlife Fund (WWF)
   • Coverage: Global vertebrate population trends, 1970–2020

Source Data:

3. Living Planet Index 2024 Dataset: Living Planet Index Technical Report
   • Source: ZSL & WWF
   • Data includes: Global, regional, and ecosystem-level index values with 95% confidence intervals
   • Index represents: 34,836 populations across 5,495 vertebrate species
   • Systems covered: Terrestrial, freshwater, and marine
   • Regions covered: Africa, Asia Pacific, Europe & Central Asia, Latin America & Caribbean, North America
   • Citation: ZSL/WWF. (2024). Living Planet Index database. Retrieved from https://www.livingplanetindex.org/

Methodology References:

4. Living Planet Index Methodology: About the LPI
   • Index values are smoothed using a 3-year running average
   • All indices are weighted by species richness across taxonomic groups
   • Baseline year: 1970 (Index = 1.0 / 100%)
   • Confidence intervals derived from bootstrap resampling

Note: This visualization reframes the index as percentage of 1970 population remaining (bars show % lost, lines show % remaining) to improve interpretability. No data transformations were applied beyond rescaling the original index values (×100).

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils